Evaluation

The evaluation metric for this competition is the macro F1 score (i.e., the unweighted mean F1). The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision $\text{p}$ and recall $\text{r}$.

Precision is the ratio of true positives $\text{tp}$ to all predicted positives $\text{tp} + \text{fp}$. Recall is the ratio of true positives $\text{tp}$ to all actual positives $\text{tp} + \text{fn}$. The F1 score is given by:

$\text{F1} = 2\frac{\text{p} \cdot \text{r}}{\text{p}+\text{r}}\ \ \mathrm{where}\ \ \text{p} = \frac{\text{tp}}{\text{tp}+\text{fp}},\ \ \text{r} = \frac{\text{tp}}{\text{tp}+\text{fn}}$

The F1 metric weighs recall and precision equally. Moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.

Submission Format

You must produce a single submission file based on test.csv containing exactly two columns: ID and LABEL.

The file should contain a header and have the following format:

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1ID,LABEL
218742,0
314108,1